Sylabus
1. Introduction Lecture + seminar Introduction to Julia2. A route toward nonlinear regression3. Neural Networks4. Recurrent Neural Networks5. Classification6. Distributional forecasting7. Midterm week - Project P1 evaluation workshops8. Time Varying Parameter (TVP) Models intro9. TVP Estimation: kernel and non-parametric statistics10. TVP Estimation: localized likelihood methods11. TVP applications in Finance12. Workshops on Project 2 progress
Anotace
Course description and Objectives:
The objective of the course is to introduce advanced methods for financial data. We will cover two main topics using machine learning including neural networks, recurrent networks, distributional networks and time varying parameter methods. Students will be able to use the modern financial econometric tools after passing this course.